111年4月28日（四）12：30，於3樓和氣會議室舉辦「研究發想會」，由台大醫學院潘思樺教授主講「發現非小細胞肺癌治療和預後之新標的：從蛋白網絡到臨床應用」，歡迎有興趣的同仁前往聆聽。

本次講座摘要：

Lung cancer is the leading cause of cancer-related death worldwide. Despite the discovery of EGFR driver mutation creating a breakthrough of lung cancer treatment, drug resistance and distant metastasis are still the ordeals that need to be solved in clinics. Here, we first show our experiences in developing novel anti-lung cancer therapeutics. Through the process of cell-based drug screening, target deconvolution and structure-activity relationship (SAR) analysis, we identified three small molecules, GRC0321, AS7128 and AS4583, which exhibited activities to inhibit NSCLC cell growth from a 2-million compound library. By chemical proteomic analyses, we not only revealed their action mechanisms, but also got information that can help us determining those who may have better responses of the drug treatments. Moreover, utilizing SAR study and in silico modeling also helped us successfully to develop hit-to-lead drug optimization. The successful integration of phenotypic screening and chemical proteome provides a novel platform that can help scientists more effectively to explore novel lung cancer therapeutics. Then, we also show you the story how SUMOylated Slug and Id4 could be used as predictive biomarkers for lung cancer metastasis. Through microarray, yeast two-hybrid and immunoprecipitation coupled to mass spectrometry (IP–MS), we interestingly found that Slug could interact with Ubc9, SUM0-1, and ID4 in lung cancer cells. Detail analyses indicated that Slug protein could be SUMOylated under hypoxia by interacting with Ubc9 and SUMO-1, interfere the transcriptional repression activity of Slug and result in promoting cancer malignancy in NSCLC. On the other hand, we found that Id4 could promote E-cadherin expression through the binding of Slug, cause the occurrence of mesenchymal-epithelial transition (MET), and inhibit cancer metastasis. The expression signatures of Slug/Ubc9 and Slug/Id4/E-cad can both be effectively used to predict the clinical outcome of lung cancer patients. Collectively, we present interesting experiences seeking novel therapeutic or diagnostic targets from the protein network to their clinical applications in NSCLC.